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New finite-time synchronization of memristive Cohen–Grossberg neural network with reaction–diffusion term based on time-varying delay

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Abstract

This paper focuses on the finite-time synchronization of memristive Cohen–Grossberg neural networks with time delays based on the reaction–diffusion term. Two new finite-time synchronous lemmas, Lemmas 2.3 and 2.4, have been obtained through some integration techniques. Since the proposal of Lemma 2.5 solves the \({X^\varphi }\left( {u\left( t \right) } \right) \) problem in the denominator, and by designing two different controllers and inequality techniques, two finite-time synchronization theorems are finally obtained. Simulations are performed according to two examples to verify the validity of the results in this paper.

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Acknowledgements

The authors thank the editor and the anonymous commentators for their precious comments and insightful suggestions, which will contribute to improve the quality of this thesis. Supported by National Natural Science Foundation of China (Grant Nos. 61374028 and 61304162).

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Authors and Affiliations

  1. College of Science, China Three Gorges University, Yichang, 443002, China

    Fangmin Ren & Xue Fang

  2. College of Science, China Three Gorges University, Yichang, 443000, Hubei, China

    Minghui Jiang

  3. College of Electrical Engineering and New Energy, China Three Gorges University, Yichang, 443002, China

    Hao Xu

Authors
  1. Fangmin Ren
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  2. Minghui Jiang
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  3. Hao Xu
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  4. Xue Fang
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Correspondence to Minghui Jiang.

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Ren, F., Jiang, M., Xu, H. et al. New finite-time synchronization of memristive Cohen–Grossberg neural network with reaction–diffusion term based on time-varying delay. Neural Comput & Applic 33, 4315–4328 (2021). https://doi.org/10.1007/s00521-020-05259-x

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